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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.KNeighborsTransformer</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-neighbors-kneighborstransformer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsTransformer</span></code></a></li>
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  <div class="section" id="sklearn-neighbors-kneighborstransformer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.KNeighborsTransformer<a class="headerlink" href="#sklearn-neighbors-kneighborstransformer" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.KNeighborsTransformer">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">KNeighborsTransformer</code><span class="sig-paren">(</span><em class="sig-param">mode='distance'</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_graph.py#L193"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X into a (weighted) graph of k nearest neighbors</p>
<p>The transformed data is a sparse graph as returned by kneighbors_graph.</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#neighbors-transformer"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>mode</strong><span class="classifier">{‘distance’, ‘connectivity’}, default=’distance’</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the connectivity
matrix with ones and zeros, and ‘distance’ will return the distances
between neighbors according to the given metric.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int, default=5</span></dt><dd><p>Number of neighbors for each sample in the transformed sparse graph.
For compatibility reasons, as each sample is considered as its own
neighbor, one extra neighbor will be computed when mode == ‘distance’.
In this case, the sparse graph contains (n_neighbors + 1) neighbors.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a></p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a></p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.fit" title="sklearn.neighbors.KNeighborsTransformer.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, default=30</span></dt><dd><p>Leaf size passed to BallTree or KDTree.  This can affect the
speed of the construction and query, as well as the memory
required to store the tree.  The optimal value depends on the
nature of the problem.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string or callable, default=’minkowski’</span></dt><dd><p>metric to use for distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.</p>
<p>If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy’s metrics, but is less
efficient than passing the metric name as a string.</p>
<p>Distance matrices are not supported.</p>
<p>Valid values for metric are:</p>
<ul class="simple">
<li><p>from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’,
‘manhattan’]</p></li>
<li><p>from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’,
‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’,
‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’,
‘yule’]</p></li>
</ul>
<p>See the documentation for scipy.spatial.distance for details on these
metrics.</p>
</dd>
<dt><strong>p</strong><span class="classifier">int, default=2</span></dt><dd><p>Parameter for the Minkowski metric from
sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=1</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
If <code class="docutils literal notranslate"><span class="pre">-1</span></code>, then the number of jobs is set to the number of CPU cores.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">Isomap</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsTransformer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">KNeighborsTransformer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;distance&#39;</span><span class="p">),</span>
<span class="gp">... </span>    <span class="n">Isomap</span><span class="p">(</span><span class="n">neighbors_algorithm</span><span class="o">=</span><span class="s1">&#39;precomputed&#39;</span><span class="p">))</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.fit" title="sklearn.neighbors.KNeighborsTransformer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit the model using X as training data</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.fit_transform" title="sklearn.neighbors.KNeighborsTransformer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.get_params" title="sklearn.neighbors.KNeighborsTransformer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors" title="sklearn.neighbors.KNeighborsTransformer.kneighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors</span></code></a>(self[, X, n_neighbors, …])</p></td>
<td><p>Finds the K-neighbors of a point.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors_graph" title="sklearn.neighbors.KNeighborsTransformer.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a>(self[, X, n_neighbors, mode])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.set_params" title="sklearn.neighbors.KNeighborsTransformer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.KNeighborsTransformer.transform" title="sklearn.neighbors.KNeighborsTransformer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Computes the (weighted) graph of Neighbors for points in X</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">mode='distance'</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">n_jobs=1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_graph.py#L282"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L1159"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model using X as training data</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, BallTree, KDTree}</span></dt><dd><p>Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric=’precomputed’.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_graph.py#L311"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ignored</span></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Xt</strong><span class="classifier">CSR sparse graph of shape (n_samples, n_samples)</span></dt><dd><p>Xt[i, j] is assigned the weight of edge that connects i to j.
Only the neighbors have an explicit value.
The diagonal is always explicit.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.kneighbors">
<code class="sig-name descname">kneighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">return_distance=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L531"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors to get (default is the value
passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Array representing the lengths to points, only present if
return_distance=True</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Indices of the nearest points in the population matrix.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1,1,1]</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]))</span>
<span class="go">(array([[0.5]]), array([[2]]))</span>
</pre></div>
</div>
<p>As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[1],</span>
<span class="go">       [2]]...)</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.kneighbors_graph">
<code class="sig-name descname">kneighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">mode='connectivity'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L705"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of k-Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors for each sample.
(default is value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 1.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.KNeighborsTransformer.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_graph.py#L291"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples_transform, n_features)</span></dt><dd><p>Sample data</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Xt</strong><span class="classifier">CSR sparse graph of shape (n_samples_transform, n_samples_fit)</span></dt><dd><p>Xt[i, j] is assigned the weight of edge that connects i to j.
Only the neighbors have an explicit value.
The diagonal is always explicit.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-neighbors-kneighborstransformer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.KNeighborsTransformer</span></code><a class="headerlink" href="#examples-using-sklearn-neighbors-kneighborstransformer" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This examples demonstrates how to precompute the k nearest neighbors before using them in KNeig..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" src="../../_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to chain KNeighborsTransformer and TSNE in a pipeline. It also shows ..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_approximate_nearest_neighbors_thumb.png" src="../../_images/sphx_glr_approximate_nearest_neighbors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py"><span class="std std-ref">Approximate nearest neighbors in TSNE</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes an..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" src="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
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